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http://dx.doi.org/10.5483/BMBRep.2011.44.4.250

Predicting tissue-specific expressions based on sequence characteristics  

Paik, Hyo-Jung (Plant Systems Engineering Center, KRIBB)
Ryu, Tae-Woo (Red Sea Laboratory for Integrative Systems Biology, Computational Biosciences Research Center, Division of Chemical & Life Sciences and Engineering, King Abdullah University of Science and Technology (KAUST))
Heo, Hyoung-Sam (Plant Systems Engineering Center, KRIBB)
Seo, Seung-Won (Plant Systems Engineering Center, KRIBB)
Lee, Do-Heon (Department of Bio and Brain Engineering, KAIST)
Hur, Cheol-Goo (Plant Systems Engineering Center, KRIBB)
Publication Information
BMB Reports / v.44, no.4, 2011 , pp. 250-255 More about this Journal
Abstract
In multicellular organisms, including humans, understanding expression specificity at the tissue level is essential for interpreting protein function, such as tissue differentiation. We developed a prediction approach via generated sequence features from overrepresented patterns in housekeeping (HK) and tissue-specific (TS) genes to classify TS expression in humans. Using TS domains and transcriptional factor binding sites (TFBSs), sequence characteristics were used as indices of expressed tissues in a Random Forest algorithm by scoring exclusive patterns considering the biological intuition; TFBSs regulate gene expression, and the domains reflect the functional specificity of a TS gene. Our proposed approach displayed better performance than previous attempts and was validated using computational and experimental methods.
Keywords
Domain; Housekeeping; Random forest; Tissue-specific; Transcription factor binding site;
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